A point-feature label placement algorithm based on spatial data mining

The point-feature label placement (PFLP) refers to the process of positioning labels near point features on a map while adhering to specific rules and guidelines, finally obtaining clear, aesthetically pleasing, and conflict-free maps. While various approaches have been suggested for automated point feature placement on maps, few studies have fully considered the spatial distribution characteristics and label correlations of point datasets, resulting in poor label quality in the process of solving the label placement of dense and complex point datasets. In this paper, we propose a point-feature label placement algorithm based on spatial data mining that analyzes the local spatial distribution characteristics and label correlations of point features. The algorithm quantifies the interference among point features by designing a label frequent pattern framework (LFPF) and constructs an ascending label ordering method based on the pattern to reduce interference. Besides, three classical metaheuristic algorithms (simulated annealing algorithm, genetic algorithm, and ant colony algorithm) are applied to the PFLP in combination with the framework to verify the validity of this framework. Additionally, a bit-based grid spatial index is proposed to reduce cache memory and consumption time in conflict detection. The performance of the experiments is tested with 4000, 10000, and 20000 points of POI data obtained randomly under various label densities. The results of these experiments showed that: (1) the proposed method outperformed both the original algorithm and recent literature, with label quality improvements ranging from 3 to 6.7 and from 0.1 to 2.6, respectively. (2) The label efficiency was improved by 58.2% compared with the traditional grid index

Algorithms for Automatic Label Placement

Práce popisuje problém automatického umísťování popisků do mapy. Jednotlivé bodové, čárové a plošné objekty v mapě je třeba označit odpovídajícími textovými či obrázkovými popisky. Tyto popisky je nutné rozmístit tak, aby se vzájemně nepřekrývaly a zároveň byly jasně přiřaditelné k odpovídajícím objektům. O problému je známo, že je NP-těžký a nalezení optimálního rozmístění všech popisků je výpočetně velmi náročné i pro nejjednodušší mapy. Pozornost je věnována umísťování popisků označujících bodové a čárové objekty, včetně prvního kroku obnášejícího přípravu možných pozic pro umístění těchto popisků, při dodržení běžných kartografických pravidel pro rozmísťování popisků. Následně jsou na problém aplikovány tři různé druhy algoritmů -- greedy ("hladové") algoritmy v kombinaci s lokálním prohledáváním, matematická optimalizace (v podobě 0-1 celočíselného programování) a genetické algoritmy. Popsané algoritmy jsou v softwarové části práce implementovány a na závěr porovnány na několika různých datových sadách, vycházejících z reálných geografických podkladů a z náhodně vygenerovaných map. Závěrečné srovnání se zaměřuje na kvalitu výsledného rozmístění (dle metrik definovaných v práci), času potřebnému k nalezení řešení a také na determinističnost daných algoritmů.Thesis describes the problem of automatic map label placement. Various point, line or area features in maps must be marked with matching text or graphic labels. These labels have to be placed so they do not overlap with each other and they are clearly associable with corresponding map features. The problem is known to be NP-hard and finding optimal positions of all map labels is highly computationally expensive, even for the simplest maps. Focus is given to the placement of labels describing point and line map features, including the initial phase of enumerating possible label positions, respecting the basic cartographic rules common for those labels. Afterwards, three different algorithm types are applied to the problem itself -- greedy algorithms (in combination with local search optimization), mathematical optimization (0-1 integer programming) and genetic algorithms. Ultimately, the described algorithms are implemented in the software part of the work and compared on various data sets, based on both real world geographical data and randomly generated maps. The final comparison focuses especially on the quality of the result (scored by the metrics defined in the thesis), time needed to find the solution and determinism of the given algorithms

Reinforced Labels: Multi-Agent Deep Reinforcement Learning for Point-Feature Label Placement

Over the recent years, Reinforcement Learning combined with Deep Learning techniques has successfully proven to solve complex problems in various domains, including robotics, self-driving cars, and finance. In this paper, we are introducing Reinforcement Learning (RL) to label placement, a complex task in data visualization that seeks optimal positioning for labels to avoid overlap and ensure legibility. Our novel point-feature label placement method utilizes Multi-Agent Deep Reinforcement Learning to learn the label placement strategy, the first machine-learning-driven labeling method, in contrast to the existing hand-crafted algorithms designed by human experts. To facilitate RL learning, we developed an environment where an agent acts as a proxy for a label, a short textual annotation that augments visualization. Our results show that the strategy trained by our method significantly outperforms the random strategy of an untrained agent and the compared methods designed by human experts in terms of completeness (i.e., the number of placed labels). The trade-off is increased computation time, making the proposed method slower than the compared methods. Nevertheless, our method is ideal for scenarios where the labeling can be computed in advance, and completeness is essential, such as cartographic maps, technical drawings, and medical atlases. Additionally, we conducted a user study to assess the perceived performance. The outcomes revealed that the participants considered the proposed method to be significantly better than the other examined methods. This indicates that the improved completeness is not just reflected in the quantitative metrics but also in the subjective evaluation by the participants

A nonmonotone GRASP

A greedy randomized adaptive search procedure (GRASP) is an itera- tive multistart metaheuristic for difficult combinatorial optimization problems. Each GRASP iteration consists of two phases: a construction phase, in which a feasible solution is produced, and a local search phase, in which a local optimum in the neighborhood of the constructed solution is sought. Repeated applications of the con- struction procedure yields different starting solutions for the local search and the best overall solution is kept as the result. The GRASP local search applies iterative improvement until a locally optimal solution is found. During this phase, starting from the current solution an improving neighbor solution is accepted and considered as the new current solution. In this paper, we propose a variant of the GRASP framework that uses a new “nonmonotone” strategy to explore the neighborhood of the current solu- tion. We formally state the convergence of the nonmonotone local search to a locally optimal solution and illustrate the effectiveness of the resulting Nonmonotone GRASP on three classical hard combinatorial optimization problems: the maximum cut prob- lem (MAX-CUT), the weighted maximum satisfiability problem (MAX-SAT), and the quadratic assignment problem (QAP)

AccuSyn: Using Simulated Annealing to Declutter Genome Visualizations

We apply Simulated Annealing, a well-known metaheuristic for obtaining near-optimal solutions to optimization problems, to discover conserved synteny relations (similar features) in genomes. The analysis of synteny gives biologists insights into the evolutionary history of species and the functional relationships between genes. However, as even simple organisms have huge numbers of genomic features, syntenic plots initially present an enormous clutter of connections, making the structure difficult to understand. We address this problem by using Simulated Annealing to minimize link crossings. Our interactive web-based synteny browser, AccuSyn, visualizes syntenic relations with circular plots of chromosomes and draws links between similar blocks of genes. It also brings together a huge amount of genomic data by integrating an adjacent view and additional tracks, to visualize the details of the blocks and accompanying genomic data, respectively. Our work shows multiple ways to manually declutter a synteny plot and then thoroughly explains how we integrated Simulated Annealing, along with human interventions as a human-in-the-loop approach, to achieve an accurate representation of conserved synteny relations for any genome. The goal of AccuSyn was to make a fairly complete tool combining ideas from four major areas: genetics, information visualization, heuristic search, and human-in-the-loop. Our results contribute to a better understanding of synteny plots and show the potential that decluttering algorithms have for syntenic analysis, adding more clues for evolutionary development. At this writing, AccuSyn is already actively used in the research being done at the University of Saskatchewan and has already produced a visualization of the recently-sequenced Wheat genome

Algorithms for Map Generation and Spatial Data Visualization in LIFE

The goal of this master thesis is to construct a software system, named the LIFE Spatial Data Visualization System (LIFE-SDVS), to automatically visualize the data obtained in the LIFE project spatially. LIFE stands for the Leipzig Research Centre for Civilization Diseases. It is part of the Medical Faculty of the University of Leipzig and conducts a large medical research project focusing on civilization diseases in the Leipzig population. Currently, more than 20,000 participants have joined this population-based cohort study. The analyses in LIFE have been mostly limited to non-spatial aspects. To integrate geographical facet into the findings, a spatial visualization tool is necessary. Hence, LIFE-SDVS, an automatic map visualization tool wrapped in an interactive web interface, is constructed. LIFE-SDVS is conceptualized with a three-layered architecture: data source, functionalities and spatial visualization layers. The implementation of LIFE-SDVS was achieved by two software components: an independent, self-contained R package lifemap and the LIFE Shiny Application. The package lifemap enables the automatic spatial visualization of statistics on the map of Leipzig and to the extent of the authors knowledge, is the first R package to achieve boundary labeling for maps. The package lifemap also contains two self-developed algorithms. The Label Positioning Algorithm was constructed to find good positions within each region on a map for placing labels, statistical graphics and as starting points for boundary label leaders. The Label Alignment Algorithm solves the leader intersection problem of boundary labeling. However, to use the plotting functions in lifemap, the users need to have basic knowledge of R and it is a tedious job to manually input the argument values whenever changes on the maps are necessary. An interactive Shiny web application, the LIFE Shiny Application, is therefore built to create a user friendly data exploration and map generation tool. LIFE Shiny Application is capable of obtaining experimental data directly from the LIFE database at runtime. Additionally, a data preprocessing unit can transform the raw data into the format needed for spatial visualization. On the LIFE Shiny Application user interface, users can specify the data to display, including what data to be fetched from database and which part of the data shall be visualized, by using the filter functions provided. Many map features are also available to improve the aesthetic presentation of the maps. The resulting maps can also be downloaded for further usage in scientific publications or reports. Two use cases using LIFE hand grip strength and body mass index data demonstrate the functionalities of LIFESDVS. The current LIFE-SDVS sets a foundation for the spatial visualization of LIFE data. Suggestions on adding further functionalities into the future version are also provided

Intelligent Systems

This book is dedicated to intelligent systems of broad-spectrum application, such as personal and social biosafety or use of intelligent sensory micro-nanosystems such as "e-nose", "e-tongue" and "e-eye". In addition to that, effective acquiring information, knowledge management and improved knowledge transfer in any media, as well as modeling its information content using meta-and hyper heuristics and semantic reasoning all benefit from the systems covered in this book. Intelligent systems can also be applied in education and generating the intelligent distributed eLearning architecture, as well as in a large number of technical fields, such as industrial design, manufacturing and utilization, e.g., in precision agriculture, cartography, electric power distribution systems, intelligent building management systems, drilling operations etc. Furthermore, decision making using fuzzy logic models, computational recognition of comprehension uncertainty and the joint synthesis of goals and means of intelligent behavior biosystems, as well as diagnostic and human support in the healthcare environment have also been made easier

Coherent Label Placement for 3D Exploded View

The use of labels in images represents the basics of visual object presentations that we are all familiar with. However, few know that automatic label placement in 2D or 3D space belongs to the set of NP-complete and NP-hard problems. While state-of-the-art algorithms such as hedgehog labeling already produce incredible coherent results in real-time interactive applications, they were only designed for static and non-deformable objects. Therefore, their performance decreases when combined with the dynamic and model-deforming the 3D model presentation techniques such as exploded diagrams a.k.a. exploded views, which present the structure of 3D model by "exploding" their parts. We propose an extension of hedgehog labeling to work with exploded views by introducing clustering of model exploded parts and their labels. Clustered hedgehog labeling uses the explosion information to separate 3D space into sections belonging to individual label clusters, each running hedgehog labeling instances solely on the cluster members. The evaluation of the proposed solution and its Textplosion implementation was done by running a usability study enhanced with eye-tracking on a group of volunteers, where improvement of the original algorithm was detected. The need for 3D test models for experimentation resulted in the creation of a 3D Labeling dataset to be shared with the community in an attempt to fill the void of a missing standardized dataset for 3D labeling algorithms